Goal:

Think, explore, & write about what the co-evolutionary interaction between newts & snakes with different genetic architectures (GAs, combination of mutation rate & mutation effect size) can lead to. This markdown is investigation what is up with the different levels of correlation between rectangles and squares (in connection with GA1 tall). After fixing the row vs column error I looked at the correlation data and found that there was less correlation. So I decided to investigate why that might be and run a few more experiments. I am running an experiment to test how changing the square size might impact the calculations. I also plan on changing the interaction rate (but want to look at the math/ feasibility of it). This file contains results discussed in Tall_GA1!

Questions:

How does cost size impact the spatial correlation of newt and snake phenotypes?

Background

Experiment

I created a simulation study to observe the co-evolutionary outcome of the newt-snake interaction with different genetic architectures (GAs) in a spatial setting. I hypothesized that we would see an interaction (co-evolutionary arms race) between newt and snake phenotype under some GA combinations when newts and snakes were evolving over geographical space. Each GA is paired with another GA creating 16 combinations.

GA1 experiment values:

Landscape: A tall map!: 35*4 H, 35 W

I tested different cost values:

Each GA combination, trial, and cost has its own msprime simulation.

The data

Preping the data by making singular files

## All cor, lit, and cost files exist!
## This program will now end!

generations

Correlation Histograms

In order to understand how spatial correlations where changing with time I took 5,000 generation time slices to look at all four trials correlation values. Each color is a different trial per GA combination. The histogram values are stacked. This section only looks at the 5 sections.

Plot 1

Plot 2

Plot 3

Plot 4

Plot 5

Plot 6

Phenotype Correlation across time

Next, we will examine three randomly chosen plots from both the 5 section and 7 section experiment. Time (in generations) in on the x-axis and both mean phenotype and phenotype spatial correlation in on the y-axis. Newt whole population mean phenotype is red, while snake mean phenotype is blue. The pink line is the phenotype spatial correlation.

Random 1 (5 sec)

Random 2 (5 sec)

Random 3 (5 sec)

Population Size Correlation across time

Random 1

Random 2

Random 3

What happens over time (looking at the beginning and late part of my simulations)

Pheno Beginning 50

Pheno Beginning 100

Pheno Beginning 150

Pheno Beginning 200

Pheno Beginning 250

Pheno End 50

Pheno End 100

Pheno End 150

Pheno End 200

Pheno End 250

Summary

In the summary section, I try to come up with a way to show how different GA combinations can change the simulations results. In all of these plots snakes GA is represented by color and newt GA is represented by shape. There 16 color-shape combinations (with 4 repeats for trials). There are four sets of plots: 1) newt by snake population size, 2) phenotype difference by snake population size, 3) phenotype difference by snake GA, and 4) phenotype difference by newt GA. There are three figures in each set, taken at the begging, middle, and end time chunks. These are whole population calculations so the 5 section and 7 section data sets are not calculated differently.

Cost: 50 Late-Sim Population Size Summary

Cost: 100 Late-Sim Population Size Summary

Cost: 150 Late-Sim Population Size Summary

Cost: 200 Late-Sim Population Size Summary

Cost: 250 Late-Sim Population Size Summary

Cost: 50 Late Difference Summary

Cost: 100 Late Difference Summary

Cost: 150 Late Difference Summary

Cost: 200 Late Difference Summary

Cost: 250 Late Difference Summary

Heatmap

Cost: Population Size (Early)

Cost: Population Size (Late)

Cost: Phenotype (Early)

Cost: Phenotype (Late)

##Grid {.tabset}

Grid Figs

50

## [1] -0.4788995

100

## [1] 0.2678364

150

## [1] -0.22375

200

## [1] 0.532094

250

## [1] 0.4848579